Typhoid Fever in Chile 1969–2012: Analysis of an Epidemic and Its Control
Bibliographic record
Abstract
in 1983). In 1983-1984, interventions were implemented focusing on person-to-person transmission (vaccination, food handlers' control, and mass communication) and regulations to control irrigation waters containing fecal contaminates. In 1991, a second intervention was quickly implemented to avoid the cholera epidemic affecting neighboring countries (total prohibition of growing or selling crops in the MR). We explored the potential impact of these interventions on the epidemic. We created a yearly database of the MR TF cases, population, and contextual factors of TF from 1969 to 2012. We first analyzed the epidemic (Joinpoint regression), identified predictors of TF (Poisson multiple regression), and then analyzed the effect of the interventions (interrupted time series model). The main predictor of the TF epidemic was the rate of unemployment. In relation to the 1983-1984 person-to-person interventions, TF came down by 51% (95% confidence interval [CI]: 30.2-65.0%) and continued to decrease at a rate of 10.4% (95% CI: 5.8-15.6%) per year until 1991. In 1991, with the strong environmental control of the sewage-irrigated crops, TF further decreased by 77% (95% CI: 69.0-83.1%) and continued decreasing thereafter at 13% (95% CI: 11.3-15.6%) per year until the end of the study period. Today, 40 years after the epidemic, TF is a rare disease in the MR of Chile.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".